Decoding Remapped Spatial Information in the Peri-Saccadic Period.

It has been suggested that, prior to a saccade, visual neurons predictively respond to stimuli that will fall in their receptive fields after completion of the saccade. This saccadic remapping process is thought to compensate for the shift of the visual world across the retina caused by eye movements. To map the timing of this predictive process in the brain, we recorded neural activity using electroencephalography (EEG) during a saccade task. Human participants (male and female) made saccades between two fixation points while covertly attending to oriented gratings briefly presented at various locations on the screen. Data recorded during trials in which participants maintained fixation were used to train classifiers on stimuli in different positions. Subsequently, data collected during saccade trials were used to test for the presence of remapped stimulus information at the post-saccadic retinotopic location in the peri-saccadic period, providing unique insight into when remapped information becomes available. We found that the stimulus could be decoded at the remapped location ∼180 ms post-stimulus onset, but only when the stimulus was presented 100-200 ms before saccade onset. Within this range, we found that the timing of remapping was dictated by stimulus onset rather than saccade onset. We conclude that presenting the stimulus immediately before the saccade allows for optimal integration of the corollary discharge signal with the incoming peripheral visual information, resulting in a remapping of activation to the relevant post-saccadic retinotopic neurons.Significance Statement Each eye movement leads to a shift of the visual world across the retina, such that the visual input before and after the eye movement do not match. Despite this, we perceive the visual world as stable. A predictive mechanism known as saccadic remapping is thought to contribute to this stability. We use a saccade task with time-resolved EEG decoding to obtain a fine-grained analysis of the temporal dynamics of the saccadic remapping process. Probing different stimulus-saccade latencies and an array of stimulus locations, we identify when remapped information becomes available in the visual cortex. We describe a critical window in which feedforward visual information and the preparatory motor signals interact to allow for predictive remapping of a stimulus.

Visual Information Is Predictively Encoded in Occipital Alpha/Low-Beta Oscillations.

Hierarchical predictive coding networks are a general model of sensory processing in the brain. Under neural delays, these networks have been suggested to naturally generate oscillatory activity in approximately the α frequency range (∼8-12 Hz). This suggests that α oscillations, a prominent feature of EEG recordings, may be a spectral "fingerprint" of predictive sensory processing. Here, we probed this possibility by investigating whether oscillations over the visual cortex predictively encode visual information. Specifically, we examined whether their power carries information about the position of a moving stimulus, in a temporally predictive fashion. In two experiments (N = 32, 18 female; N = 34, 17 female), participants viewed an apparent-motion stimulus moving along a circular path while EEG was recorded. To investigate the encoding of stimulus-position information, we developed a method of deriving probabilistic spatial maps from oscillatory power estimates. With this method, we demonstrate that it is possible to reconstruct the trajectory of a moving stimulus from α/low-ß oscillations, tracking its position even across unexpected motion reversals. We also show that future position representations are activated in the absence of direct visual input, demonstrating that temporally predictive mechanisms manifest in α/ß band oscillations. In a second experiment, we replicate these findings and show that the encoding of information in this range is not driven by visual entrainment. By demonstrating that occipital α/ß oscillations carry stimulus-related information, in a temporally predictive fashion, we provide empirical evidence of these rhythms as a spectral "fingerprint" of hierarchical predictive processing in the human visual system.SIGNIFICANCE STATEMENT "Hierarchical predictive coding" is a general model of sensory information processing in the brain. When in silico predictive coding models are constrained by neural transmission delays, their activity naturally oscillates in roughly the α range (∼8-12 Hz). Using time-resolved EEG decoding, we show that neural rhythms in this approximate range (α/low-ß) over the human visual cortex predictively encode the position of a moving stimulus. From the amplitude of these oscillations, we are able to reconstruct the stimulus' trajectory, revealing signatures of temporally predictive processing. This provides direct neural evidence linking occipital α/ß rhythms to predictive visual processing, supporting the emerging view of such oscillations as a potential spectral "fingerprint" of hierarchical predictive processing in the human visual system.

Spike-timing dependent plasticity partially compensates for neural delays in a multi-layered network of motion-sensitive neurons.

The ability of the brain to represent the external world in real-time is impacted by the fact that neural processing takes time. Because neural delays accumulate as information progresses through the visual system, representations encoded at each hierarchical level are based upon input that is progressively outdated with respect to the external world. This 'representational lag' is particularly relevant to the task of localizing a moving object-because the object's location changes with time, neural representations of its location potentially lag behind its true location. Converging evidence suggests that the brain has evolved mechanisms that allow it to compensate for its inherent delays by extrapolating the position of moving objects along their trajectory. We have previously shown how spike-timing dependent plasticity (STDP) can achieve motion extrapolation in a two-layer, feedforward network of velocity-tuned neurons, by shifting the receptive fields of second layer neurons in the opposite direction to a moving stimulus. The current study extends this work by implementing two important changes to the network to bring it more into line with biology: we expanded the network to multiple layers to reflect the depth of the visual hierarchy, and we implemented more realistic synaptic time-courses. We investigate the accumulation of STDP-driven receptive field shifts across several layers, observing a velocity-dependent reduction in representational lag. These results highlight the role of STDP, operating purely along the feedforward pathway, as a developmental strategy for delay compensation.

Blurred Lines: Memory, Perceptions, and Consciousness: Commentary on "Consciousness as a Memory System" by Budson et al (2022).

In the previous issue, Budson, Richman, and Kensinger (2022) put forth the intriguing proposal that consciousness may have evolved from the episodic memory system. In addition to providing a possible evolutionary trajectory for consciousness, I believe that viewing consciousness as an extension of memory in this way is particularly useful for understanding some of the puzzling temporal complexities that are inherent to consciousness. For example, due to neural transmission delays, our conscious experience must necessarily lag the outside world, which creates a paradox for both conscious perception (Do we see the past, rather than the present?) and action (How can we make rapid decisions if it takes so long to become conscious of something?). These paradoxes can be elegantly solved by treating consciousness as a memory system. Finally, the proposal put forth by Budson and colleagues (2022) aligns with the emerging perspective that consciousness, like memory, represents a narrative time line of events rather than any single instant. However, I believe that this conceptualization can be further extended to include not only the past, but also the future. In this way, consciousness can be provocatively viewed as the remembered past, present, and future.

Predictions drive neural representations of visual events ahead of incoming sensory information.

The transmission of sensory information through the visual system takes time. As a result of these delays, the visual information available to the brain always lags behind the timing of events in the present moment. Compensating for these delays is crucial for functioning within dynamic environments, since interacting with a moving object (e.g., catching a ball) requires real-time localization of the object. One way the brain might achieve this is via prediction of anticipated events. Using time-resolved decoding of electroencephalographic (EEG) data, we demonstrate that the visual system represents the anticipated future position of a moving object, showing that predictive mechanisms activate the same neural representations as afferent sensory input. Importantly, this activation is evident before sensory input corresponding to the stimulus position is able to arrive. Finally, we demonstrate that, when predicted events do not eventuate, sensory information arrives too late to prevent the visual system from representing what was expected but never presented. Taken together, we demonstrate how the visual system can implement predictive mechanisms to preactivate sensory representations, and argue that this might allow it to compensate for its own temporal constraints, allowing us to interact with dynamic visual environments in real time.

Exploring the extent to which shared mechanisms contribute to motion-position illusions.

Motion-position illusions (MPIs) are visual motion illusions in which motion signals bias the perceived position of an object. Due to phenomenological similarities between these illusions, previous research has assumed that some are caused by common mechanisms. However, this assumption has yet to be directly tested. This study investigates this assumption by exploiting between-participant variations in illusion magnitude. During two sessions, 106 participants viewed the flash-lag effect, luminance flash-lag effect, Fröhlich effect, flash-drag effect, flash-grab effect, motion-induced position shift, twinkle-goes effect, and the flash-jump effect. For each effect, the magnitude of the illusion was reliable within participants, strongly correlating between sessions. When the pairwise correlations of averaged illusions magnitudes were explored, two clusters of statistically significant positively correlated illusions were identified. The first cluster comprised the flash-grab effect, motion-induced position shift, and twinkle-goes effect. The second cluster comprised the Fröhlich and flash-drag effect. The fact that within each of these two clusters, individual differences in illusion magnitude were correlated suggests that these clusters may reflect shared underlying mechanisms. An exploratory factor analysis provided additional evidence that these correlated clusters shared an underlying factor, with each cluster loading onto their own factor. Overall, our results reveal that, contrary to the prevailing perspective in the literature, while some motion-position illusions share processes, most of these illusions are unlikely to reflect any shared processes, instead implicating unique mechanisms.

Predictive Visual Motion Extrapolation Emerges Spontaneously and without Supervision at Each Layer of a Hierarchical Neural Network with Spike-Timing-Dependent Plasticity.

The fact that the transmission and processing of visual information in the brain takes time presents a problem for the accurate real-time localization of a moving object. One way this problem might be solved is extrapolation: using an object's past trajectory to predict its location in the present moment. Here, we investigate how a simulated in silico layered neural network might implement such extrapolation mechanisms, and how the necessary neural circuits might develop. We allowed an unsupervised hierarchical network of velocity-tuned neurons to learn its connectivity through spike-timing-dependent plasticity (STDP). We show that the temporal contingencies between the different neural populations that are activated by an object as it moves causes the receptive fields of higher-level neurons to shift in the direction opposite to their preferred direction of motion. The result is that neural populations spontaneously start to represent moving objects as being further along their trajectory than where they were physically detected. Because of the inherent delays of neural transmission, this effectively compensates for (part of) those delays by bringing the represented position of a moving object closer to its instantaneous position in the world. Finally, we show that this model accurately predicts the pattern of perceptual mislocalization that arises when human observers are required to localize a moving object relative to a flashed static object (the flash-lag effect; FLE).SIGNIFICANCE STATEMENT Our ability to track and respond to rapidly changing visual stimuli, such as a fast-moving tennis ball, indicates that the brain is capable of extrapolating the trajectory of a moving object to predict its current position, despite the delays that result from neural transmission. Here, we show how the neural circuits underlying this ability can be learned through spike-timing-dependent synaptic plasticity and that these circuits emerge spontaneously and without supervision. This demonstrates how the neural transmission delays can, in part, be compensated to implement the extrapolation mechanisms required to predict where a moving object is at the present moment.

Motion Extrapolation in Visual Processing: Lessons from 25 Years of Flash-Lag Debate.

Because of the delays inherent in neural transmission, the brain needs time to process incoming visual information. If these delays were not somehow compensated, we would consistently mislocalize moving objects behind their physical positions. Twenty-five years ago, Nijhawan used a perceptual illusion he called the flash-lag effect (FLE) to argue that the brain's visual system solves this computational challenge by extrapolating the position of moving objects (Nijhawan, 1994). Although motion extrapolation had been proposed a decade earlier (e.g., Finke et al., 1986), the proposal that it caused the FLE and functioned to compensate for computational delays was hotly debated in the years that followed, with several alternative interpretations put forth to explain the effect. Here, I argue, 25 years later, that evidence from behavioral, computational, and particularly recent functional neuroimaging studies converges to support the existence of motion extrapolation mechanisms in the visual system, as well as their causal involvement in the FLE. First, findings that were initially argued to challenge the motion extrapolation model of the FLE have since been explained, and those explanations have been tested and corroborated by more recent findings. Second, motion extrapolation explains the spatial shifts observed in several FLE conditions that cannot be explained by alternative (temporal) models of the FLE. Finally, neural mechanisms that actually perform motion extrapolation have been identified at multiple levels of the visual system, in multiple species, and with multiple different methods. I outline key questions that remain, and discuss possible directions for future research.

Perceptual integration without conscious access.

The visual system has the remarkable ability to integrate fragmentary visual input into a perceptually organized collection of surfaces and objects, a process we refer to as perceptual integration. Despite a long tradition of perception research, it is not known whether access to consciousness is required to complete perceptual integration. To investigate this question, we manipulated access to consciousness using the attentional blink. We show that, behaviorally, the attentional blink impairs conscious decisions about the presence of integrated surface structure from fragmented input. However, despite conscious access being impaired, the ability to decode the presence of integrated percepts remains intact, as shown through multivariate classification analyses of electroencephalogram (EEG) data. In contrast, when disrupting perception through masking, decisions about integrated percepts and decoding of integrated percepts are impaired in tandem, while leaving feedforward representations intact. Together, these data show that access consciousness and perceptual integration can be dissociated.

Motion extrapolation in the High-Phi illusion: Analogous but dissociable effects on perceived position and perceived motion.

A range of visual illusions, including the much-studied flash-lag effect, demonstrate that neural signals coding for motion and position interact in the visual system. One interpretation of these illusions is that they are the consequence of motion extrapolation mechanisms in the early visual system. Here, we study the recently reported High-Phi illusion to investigate whether it might be caused by the same underlying mechanisms. In the High-Phi illusion, a rotating texture is abruptly replaced by a new, uncorrelated texture. This leads to the percept of a large illusory jump, which can be forward or backward depending on the duration of the initial motion sequence (the inducer). To investigate whether this motion illusion also leads to illusions of perceived position, in three experiments we asked observers to localize briefly flashed targets presented concurrently with the new texture. Our results replicate the original finding of perceived forward and backward jumps, and reveal an illusion of perceived position. Like the observed effects on illusory motion, these position shifts could be forward or backward, depending on the duration of the inducer: brief inducers caused forward mislocalization, and longer inducers caused backward mislocalization. Additionally, we found that both jumps and mislocalizations scaled in magnitude with the speed of the inducer. Interestingly, forward position shifts were observed at shorter inducer durations than forward jumps. We interpret our results as an interaction of extrapolation and correction-for-extrapolation, and discuss possible mechanisms in the early visual system that might carry out these computations.

Motion Extrapolation for Eye Movements Predicts Perceived Motion-Induced Position Shifts.

Transmission delays in the nervous system pose challenges for the accurate localization of moving objects as the brain must rely on outdated information to determine their position in space. Acting effectively in the present requires that the brain compensates not only for the time lost in the transmission and processing of sensory information, but also for the expected time that will be spent preparing and executing motor programs. Failure to account for these delays will result in the mislocalization and mistargeting of moving objects. In the visuomotor system, where sensory and motor processes are tightly coupled, this predicts that the perceived position of an object should be related to the latency of saccadic eye movements aimed at it. Here we use the flash-grab effect, a mislocalization of briefly flashed stimuli in the direction of a reversing moving background, to induce shifts of perceived visual position in human observers (male and female). We find a linear relationship between saccade latency and perceived position shift, challenging the classic dissociation between "vision for action" and "vision for perception" for tasks of this kind and showing that oculomotor position representations are either shared with or tightly coupled to perceptual position representations. Altogether, we show that the visual system uses both the spatial and temporal characteristics of an upcoming saccade to localize visual objects for both action and perception.SIGNIFICANCE STATEMENT Accurately localizing moving objects is a computational challenge for the brain due to the inevitable delays that result from neural transmission. To solve this, the brain might implement motion extrapolation, predicting where an object ought to be at the present moment. Here, we use the flash-grab effect to induce perceptual position shifts and show that the latency of imminent saccades predicts the perceived position of the objects they target. This counterintuitive finding is important because it not only shows that motion extrapolation mechanisms indeed work to reduce the behavioral impact of neural transmission delays in the human brain, but also that these mechanisms are closely matched in the perceptual and oculomotor systems.

When predictions fail: Correction for extrapolation in the flash-grab effect.

Motion-induced position shifts constitute a broad class of visual illusions in which motion and position signals interact in the human visual pathway. In such illusions, the presence of visual motion distorts the perceived positions of objects in nearby space. Predictive mechanisms, which could contribute to compensating for processing delays due to neural transmission, have been given as an explanation. However, such mechanisms have struggled to explain why we do not usually perceive objects extrapolated beyond the end of their trajectory. Advocates of this interpretation have proposed a "correction-for-extrapolation" mechanism to explain this: When the object motion ends abruptly, this mechanism corrects the overextrapolation by shifting the perceived object location backwards to its actual location. However, such a mechanism has so far not been empirically demonstrated. Here, we use a novel version of the flash-grab illusion to demonstrate this mechanism. In the flash-grab effect, a target is flashed on a moving background that abruptly changes direction, leading to the mislocalization of the target. Here, we manipulate the angle of the direction change to dissociate the contributions of the background motion before and after the flash. Consistent with previous reports, we observe that perceptual mislocalization in the flash-grab illusion is mainly driven by motion after the flash. Importantly, however, we reveal a small but consistent mislocalization component in the direction opposite to the direction of the first motion sequence. This provides empirical support for the proposed correction-for-extrapolation mechanism, and therefore corroborates the interpretation that motion-induced position shifts might result from predictive interactions between motion and position signals.

Predictive coding of visual motion in both monocular and binocular human visual processing.

Neural processing of sensory input in the brain takes time, and for that reason our awareness of visual events lags behind their actual occurrence. One way the brain might compensate to minimize the impact of the resulting delays is through extrapolation. Extrapolation mechanisms have been argued to underlie perceptual illusions in which moving and static stimuli are mislocalised relative to one another (such as the flash-lag and related effects). However, where in the visual hierarchy such extrapolation processes take place remains unknown. Here, we address this question by identifying monocular and binocular contributions to the flash-grab illusion. In this illusion, a brief target is flashed on a moving background that reverses direction. As a result, the perceived position of the target is shifted in the direction of the reversal. We show that the illusion is attenuated, but not eliminated, when the motion reversal and the target are presented dichoptically to separate eyes. This reveals extrapolation mechanisms at both monocular and binocular processing stages contribute to the illusion. We interpret the results in a hierarchical predictive coding framework, and argue that prediction errors in this framework manifest directly as perceptual illusions.

Expecting the unexpected: Temporal expectation increases the flash-grab effect.

In the flash-grab effect, when a disk is flashed on a moving background at the moment it reverses direction, the perceived location of the disk is strongly displaced in the direction of the motion that follows the reversal. Here, we ask whether increased expectation of the reversal reduces its effect on the motion-induced shift, as suggested by predictive coding models with first order predictions. Across four experiments we find that when the reversal is expected, the illusion gets stronger, not weaker. We rule out accumulating motion adaptation as a contributing factor. The pattern of results cannot be accounted for by first-order predictions of location. Instead, it appears that second-order predictions of event timing play a role. Specifically, we conclude that temporal expectation causes a transient increase in temporal attention, boosting the strength of the motion signal and thereby increasing the strength of the illusion.

Predictive coding of visual object position ahead of moving objects revealed by time-resolved EEG decoding.

Due to the delays inherent in neuronal transmission, our awareness of sensory events necessarily lags behind the occurrence of those events in the world. If the visual system did not compensate for these delays, we would consistently mislocalize moving objects behind their actual position. Anticipatory mechanisms that might compensate for these delays have been reported in animals, and such mechanisms have also been hypothesized to underlie perceptual effects in humans such as the Flash-Lag Effect. However, to date no direct physiological evidence for anticipatory mechanisms has been found in humans. Here, we apply multivariate pattern classification to time-resolved EEG data to investigate anticipatory coding of object position in humans. By comparing the time-course of neural position representation for objects in both random and predictable apparent motion, we isolated anticipatory mechanisms that could compensate for neural delays when motion trajectories were predictable. As well as revealing an early neural position representation (lag 80-90 ms) that was unaffected by the predictability of the object's trajectory, we demonstrate a second neural position representation at 140-150 ms that was distinct from the first, and that was pre-activated ahead of the moving object when it moved on a predictable trajectory. The latency advantage for predictable motion was approximately 16 ±â€¯2 ms. To our knowledge, this provides the first direct experimental neurophysiological evidence of anticipatory coding in human vision, revealing the time-course of predictive mechanisms without using a spatial proxy for time. The results are numerically consistent with earlier animal work, and suggest that current models of spatial predictive coding in visual cortex can be effectively extended into the temporal domain.

Interpreting the dimensions of neural feature representations revealed by dimensionality reduction.

Recent progress in understanding the structure of neural representations in the cerebral cortex has centred around the application of multivariate classification analyses to measurements of brain activity. These analyses have proved a sensitive test of whether given brain regions provide information about specific perceptual or cognitive processes. An exciting extension of this approach is to infer the structure of this information, thereby drawing conclusions about the underlying neural representational space. These approaches rely on exploratory data-driven dimensionality reduction to extract the natural dimensions of neural spaces, including natural visual object and scene representations, semantic and conceptual knowledge, and working memory. However, the efficacy of these exploratory methods is unknown, because they have only been applied to representations in brain areas for which we have little or no secondary knowledge. One of the best-understood areas of the cerebral cortex is area MT of primate visual cortex, which is known to be important in motion analysis. To assess the effectiveness of dimensionality reduction for recovering neural representational space we applied several dimensionality reduction methods to multielectrode measurements of spiking activity obtained from area MT of marmoset monkeys, made while systematically varying the motion direction and speed of moving stimuli. Despite robust tuning at individual electrodes, and high classifier performance, dimensionality reduction rarely revealed dimensions for direction and speed. We use this example to illustrate important limitations of these analyses, and suggest a framework for how to best apply such methods to data where the structure of the neural representation is unknown.

Voluntary Saccadic Eye Movements Ride the Attentional Rhythm.

Visual perception seems continuous, but recent evidence suggests that the underlying perceptual mechanisms are in fact periodic-particularly visual attention. Because visual attention is closely linked to the preparation of saccadic eye movements, the question arises how periodic attentional processes interact with the preparation and execution of voluntary saccades. In two experiments, human observers made voluntary saccades between two placeholders, monitoring each one for the presentation of a threshold-level target. Detection performance was evaluated as a function of latency with respect to saccade landing. The time course of detection performance revealed oscillations at around 4 Hz both before the saccade at the saccade origin and after the saccade at the saccade destination. Furthermore, oscillations before and after the saccade were in phase, meaning that the saccade did not disrupt or reset the ongoing attentional rhythm. Instead, it seems that voluntary saccades are executed as part of an ongoing attentional rhythm, with the eyes in flight during the troughs of the attentional wave. This finding for the first time demonstrates that periodic attentional mechanisms affect not only perception but also overt motor behavior.

Self-touch modulates the somatosensory evoked P100.

It has recently been shown that contact between one's own limbs (self-touch) reduces the perceived intensity of pain, over and above the well-known modulation of pain by simultaneous colocalized tactile input Kammers et al. (Curr Biol 20:1819-1822, 2010). Here, we investigate how self-touch modulates somatosensory evoked potentials (SEPs) evoked by afferent somatosensory input. We show that the P100 SEP component, which has previously been implicated in the conscious perception of a tactile stimulus, is enhanced during self-touch, as compared to when one is touching nothing, an inanimate object, or another person. A follow-up experiment showed that there was no effect of self-touch on SEPs when the body parts in contact were not symmetric. Altogether, our findings suggest the interpretation that the secondary somatosensory cortex might underlie the specific analgesic effect of self-touch.

Neural mechanisms of visual motion extrapolation.

Because neural processing takes time, the brain only has delayed access to sensory information. When localising moving objects this is problematic, as an object will have moved on by the time its position has been determined. Here, we consider predictive motion extrapolation as a fundamental delay-compensation strategy. From a population-coding perspective, we outline how extrapolation can be achieved by a forwards shift in the population-level activity distribution. We identify general mechanisms underlying such shifts, involving various asymmetries which facilitate the targeted 'enhancement' and/or 'dampening' of population-level activity. We classify these on the basis of their potential implementation (intra- vs inter-regional processes) and consider specific examples in different visual regions. We consider how motion extrapolation can be achieved during inter-regional signaling, and how asymmetric connectivity patterns which support extrapolation can emerge spontaneously from local synaptic learning rules. Finally, we consider how more abstract 'model-based' predictive strategies might be implemented. Overall, we present an integrative framework for understanding how the brain determines the real-time position of moving objects, despite neural delays.

Decoding the motion aftereffect in human visual cortex.

In the motion aftereffect (MAE), adapting to a moving stimulus causes a subsequently presented stationary stimulus to appear to move in the opposite direction. Recently, the neural basis of the motion aftereffect has received considerable interest, and a number of brain areas have been implicated in the generation of the illusory motion. Here, we use functional magnetic resonance imaging in combination with multivariate pattern classification to directly compare the neural activity evoked during the observation of both real and illusory motions. We show that the perceived illusory motion is not encoded in the same way as real motion in the same direction. Instead, suppression of the adapted direction of motion results in a shift of the population response of motion sensitive neurons in area MT+, resulting in activation patterns that are in fact more similar to real motion in orthogonal, rather than opposite directions. Although robust motion selectivity was observed in visual areas V1, V2, V3, and V4, this MAE-specific modulation of the population response was only observed in area MT+. Implications for our understanding of the motion aftereffect, and models of motion perception in general, are discussed.